Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization

The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.

Table of Contents:
Introduction Evolution of trading infrastructure Quantitative strategies and time-scalesStatistical arbitrage and debates about EMH Quantitative funds, mutual funds, hedge fundsData, analytics, models, optimization, algorithms Interdisciplinary nature of the subject and how the book can be used Supplements and problems Statistical Models and Methods for Quantitative Trading Stylized facts on stock price data Time series of low-frequency returnsDiscrete price changes in high-frequency dataBrownian motion at the Paris Exchange and random walk down Wall Street MPT as a \walking shoe down Wall Street Statistical underpinnings of MPT Multifactor pricing models Bayes, shrinkage, and Black-Litterman estimatorsBootstrapping and the resampled frontierA new approach incorporating parameter uncertainty Solution of the optimization problem Computation of the optimal weight vector Bootstrap estimate of performance and NPEBFrom random walks to martingales that match stylized facts From Gaussian to Paretian random walksRandom walks with optional sampling timesFrom random walks to ARIMA, GARCH Neo-MPT involving martingale regression modelsIncorporating time series e_ects in NPEB Optimizing information ratios along e_cient frontier An empirical study of neo-MPT Statistical arbitrage and strategies beyond EMH Technical rules and the statistical backgroundTime series, momentum, and pairs trading strategies Contrarian strategies, behavioral _nance, and investors' cognitive biases From value investing to global macro strategies In-sample and out-of-sample evaluationSupplements and problems Active Por

About the Author :
Xin Guo is the Coleman Fung Chair Professor of Financial Modeling in the department of Industrial Engineering and Operations Research, UC Berkeley. She founded the Berkeley Risk Analysis and Data Analytics Research (RADAR) Lab and holds a courtesy appointment with the Lawrence Berkeley National Lab. Prior to UC Berkeley, she was a Research Staff Member at the IBM T. J. Watson Research Center and an Associate Professor at Cornell University. Her main research interests are stochastic control, stochastic processes and applications. In addition to high frequency trading modeling and analysis, her recent research includes singular controls, impulse controls, non-linear expectations, mean-field games, and filtration enlargement with application to credit risk. Tze Leung Lai is a Professor of Statistics and, by courtesy, of Health Research and Policy in the School of Medicine and of the Institute for Computational & Mathematical Engineering (ICME) in the School of Engineering at Stanford University. He is Director of the Financial and Risk Modeling Institute, Co-Director of the Biostatistics Core of the Stanford Cancer Institute, and Co-Director of the Center for Innovative Study Design at the Stanford School of Medicine. He has held regular and visiting faculty appointments at Columbia University, UC Berkeley, and Nankai University, and holds advisory positions with the University of Hong Kong, Peking University, and Tsinghua University. Howard Shek is a senior researcher at Tower Research Capital, where he has built and led the Core Research team with a mandate that covers the wide spectrum of research topics in automated trading. He has over 15 years of quantitative research and trading experience in fixed-income arbitrage, market microstructure, volatility estimation, option pricing, and portfolio theory, and has held senior trading and research positions at Merrill Lynch and J. P. Morgan, focusing on proprietary trading in fixed-income derivatives. Samuel Po-Shing Wong is CEO and Chief Quant of 5Lattice Securities, a proprietary trading company in Hong Kong that develops quantitative trading algorithms and corresponding risk management methodologies from statistical data analysis and machine learning. He also teaches the course of Algorithmic Trading for Stanford Quantitative Finance Program in Hong Kong and serves as an Honorary Professor of the Department of Statistics and Actuarial Science at The University of Hong Kong.

Review :
"All in all, it is certainly a welcome addition to the nascent literature on this intriguing subject and recommended reading for those interested in quantitative trading strategies—academics, practitioners, and students alike." ~The American Statistician, Mikko S. Pakkanen